2%, 26.4% 31.9 %, 13.6%, 1.85% and 1.6% respectively. Conclusion Considering the high prevalence of obesity and smoking in the population of Hoveyzeh and since the important role of these risk factors in development of common non communicable diseases, this issue should be taken into consideration and the necessary interventions in this context must be considered to modify lifestyle. The HCS is the only comprehensive cohort in the region, enabling it to provide valuable evidence about NCDs for a wide geographical area covering millions of people in both Iran and Iraq.Background Breast cancer is one of the most causes of death in women. Early diagnosis and detection of Invasive Ductal Carcinoma (IDC) is an important key for the treatment of IDC. Computer-aided approaches have great potential to improve diagnosis accuracy. In this paper, we proposed a deep learning-based method for the automatic classification of IDC in whole slide images (WSI) of breast cancer. Furthermore, different types of deep neural networks training such as training from scratch and transfer learning to classify IDC were evaluated. Methods In total, 277524 image patches with 50×50-pixel size form original images were used for model training. In the first method, we train a simple convolutional neural network (named it baseline model) on these images. In the second approach, we used the pre-trained VGG-16 CNN model via feature extraction and fine-tuning for the classification of breast pathology images. Results Our baseline model achieved a better result for the automatic classification of IDC in terms of F-measure and accuracy (83%, 85%) in comparison with original paper on this data set and achieved a comparable result with a new study that introduced accepted-rejected pooling layer. Also, transfer learning via feature extraction yielded better results (81%, 81%) in comparison with handcrafted features. Furthermore, transfer learning via feature extraction yielded better classification results in comparison with the baseline model. Conclusion The experimental results demonstrate that using deep learning approaches yielded better results in comparison with handcrafted features. Also, using transfer learning in histopathology image analysis yielded significant results in comparison with training from scratch in much less time.Background Successful aging is a prominent and worldwide theme in gerontology. However, until recently, only few studies were conducted about successful aging in Iran. This study examined whether a healthy lifestyle could predict successful aging among older Iranians. Methods This cross sectional and descriptive study included 975 older Iranians who were selected through a multistage cluster-quota method from the health centers of Shiraz, Iran. A 5-part questionnaire, including demographic characteristics, the Seniors' Healthy Lifestyle, Barthel Index, the Diner Life Satisfaction and Quality of Life, was used to collect the data. A logistic regression analysis was used in data analysis; data were analyzed using SPSS 21; and significance level was set at α = 0.05. Results The prevalence of successful aging among older Iranians was calculated at 24.0%. Results of multiple logistic regression analysis revealed that age (95% CI = 1.129- 1.702 and OR = 1.352), gender (95% CI = 0.412-0.764 and OR = 0.687), education level (95% CI = 1.443 - 1.699 and OR = 1.454), job (95% CI = 1.063-1.413 and OR = 1.185), monthly income (95% CI = 1.355-4.055 and OR = 2.272), insurance (95% CI = 0.344-0.842 and OR = 0.540), source of income (95% CI = 1.014-1.298 and OR = 1.145), and healthy lifestyle (95% CI = 0.772 - 0.858 and OR = 0.814) were predictors for successful aging. Conclusion Findings indicated that successful agers were mostly younger men, with higher education level and monthly income, who had insurance and a job and a healthy lifestyle. Thus, to age successfully, one must maintain and improve healthy lifestyle to prolong one's health.Background Street children around the world are accompanying a wide range of risky behaviors. The most common ones include risky sexual behavior, substance and alcohol abuse, and violence. This study aimed to assess risk behaviors and HIV knowledge of street children in Shiraz. Methods A total of 329 street children (7-18 years of age who spend days or nights on streets with or without their family for earning money) were interviewed through 2014-2016 in Shiraz. Data were collected through a structured interview about high-risk behaviors and HIV/AIDS Knowledge based on a form and questionnaire. Street children were asked to identify HIV/AIDS mode of transmission. All correct answers were scored as one (1), and incorrect, "don't know" responses and no responses scored as zero. The data were analyzed by SPSS software 16 (SPSS, Inc. Chicago, USA) using the Independent t-test and chi-square test, and Pearson's correlation test. P value less then 0.05 was considered as statistically significant Results The mean ± SD age was 13.46±3.09. A total of 86.6% of them were boys. A total of 97.6% of them reported staying with their parents. Street children reported sleeping place as follow with their parents (n=312, 94.8%), sharing accommodation with other kids (n=13, %4), sleeping in parks (n=2, 7%), and one with relatives. The frequency of smoking, alcohol drinking, and drug abuse were 35 (10.6%), 47 (14. 3%), and 6 (1.8%) respectively. A total of 43 (13.1%) street children reported sexual activity, among them 30 (9.1%) had sexual activity without a condom. Mean ± SD HIV/AIDS knowledge scoring of street children was, 4.1±3.9. Conclusion Special programs should be implemented in order to reduce high-risk behavior among street children. Intervention should include increasing awareness about alcohol and drug abuse, HIV/AIDS knowledge, sexual and verbal abuse through an organized system with the help of peer education.Background Iran has been faced with an emerging epidemic of methamphetamine (MA) use during recent years. No effective pharmacotherapy has been identified for MA treatment; and psychological interventions are the only available effective treatment. The aim of this study is to investigate the efficacy and safety of extended-release methylphenidate (ER-MTP) for the treatment of methamphetamine dependence. Methods Sixty-two people with methamphetamine dependence, according to DSM-IV-TR, were randomly assigned to either fixed-dose extended-release methylphenidate (ER-MTP) (60 mg per day) or placebo for 12 weeks. https://www.selleckchem.com/products/jbj-09-063-hydrochloride.html All participants received twice-weekly cognitive behavioral treatment for stimulant dependence. Recent drug use and craving level were measured using weekly rapid urine test and craving visual analogue scale, respectively. The severity of addiction was measured using the Addiction Severity Index at baseline and study completion. Assessment of MA withdrawal was conducted using Amphetamine Withdrawal Questionnaire and Amphetamine Selective Severity Assessment at baseline, day 3, week 1, week 4 and week 12.